Nuclear + HPC and AI at ORNL
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What it means
Nuclear energy science is incredibly complex due to the interplay of multi-scale physical phenomena — from quantum interactions to reactor-level energy production — intricate fission, decay, and neutron reactions; operations within extremely high temperature, pressure, and radiation environments; and significant uncertainties. Oak Ridge National Laboratory integrates advanced computing and nuclear energy research to drive innovations critical to the nation’s energy future. By combining leadership-class computing, artificial intelligence, and multi-scale modeling, we can tackle challenges in nuclear energy design, safety, and sustainability.
Why it matters
High-performance computing and AI technologies provide the necessary computational power, data-processing capabilities, and advanced modeling techniques to simulate, optimize, and manage these complexities—leading to safer, more efficient nuclear energy systems.
What we do at ORNL
One of ORNL’s cornerstone tools, the Oak Ridge Siting Analysis for power Generation Expansion (OR-SAGE), revolutionizes decisions about siting energy facilities—including nuclear power plants—across the U.S. By incorporating over 70 geospatial data layers, OR-SAGE provides science-based, forward-looking solutions that streamline site selection while balancing feasibility, environmental stewardship, and regulatory compliance.
On the modeling side, cutting-edge tools like the Virtual Environment for Reactor Analysis (VERA) and Shift enable high-fidelity simulations of nuclear reactors, empowering researchers to optimize reactor performance, reduce costs, and improve safety.
ORNL’s unique capabilities also extend to shaping the future of nuclear innovation. Advanced modeling frameworks and AI/ML algorithms accelerate the design of next-generation materials, such as radiation-resistant alloys and molten salts, which are crucial for fission and fusion systems. With expertise spanning scales—from quantum-level chemistry to full-scale reactor simulations—ORNL is building digital twins of nuclear facilities and enabling breakthroughs in fusion science through tools like MATEY, a transformative fluid dynamics foundation model.
What’s next
What sets ORNL apart is its seamless integration of disciplines—from materials science and isotopes to manufacturing and AI—backed by decades of collaboration with the NRC and leadership in the Exascale Computing Project. With its unparalleled expertise and resources, ORNL is uniquely positioned to make nuclear energy safer, faster to deploy, and more cost-effective, helping to secure a sustainable energy future for the nation and the world.
CASL aids startup of TVA’s Watts Bar Unit 2
CASL aids startup of TVA’s Watts Bar Unit 2
Using data supplied by partners TVA and the Westinghouse Electric Company and high-performance computing (HPC) resources managed by the Oak Ridge Leadership Computing Facility, CASL carried out the largest time-dependent simulation of a nuclear power plant to date. The simulations confirmed engineers’ predictions related to the safe and reliable operation of Watts Bar Unit 2 — including when the reactor would sustain a fission reaction—and provided a detailed picture of the reactor’s hour-by-hour behavior during power escalation.
Accelerating nuclear licensing with AI
Accelerating nuclear licensing with AI
Using ORNL’s Frontier, the world’s first exascale supercomputer, Atomic Canyon developed novel AI models designed specifically for the nuclear industry called FERMI, which powers Atomic Canyon’s Neutron AI platform. FERMI models enable intelligent search capabilities, allowing users to quickly locate relevant documents across vast repositories of technical documentation. The computing power of Frontier was necessary to teach models the technical language of the nuclear industry based on the vocabulary used in the more than 53 million pages of nuclear documents contained within the NRC’s ADAMS database, the NRC’s official record-keeping system that documents the history of every reactor in the country.
